Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Authors: Yuchen Xiao, Weihao Tan, Christopher Amato
NeurIPS 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results (in simulation and hardware) in a variety of realistic domains demonstrate the superiority of our approaches in large multi-agent problems and validate the effectiveness of our algorithms for learning high-quality and asynchronous solutions. |
| Researcher Affiliation | Academia | Yuchen Xiao Khoury College of Computer Sciences Northeastern University Boston, MA 02115 EMAIL Weihao Tan Khoury College of Computer Sciences Northeastern University Boston, MA 02115 EMAIL Christopher Amato Khoury College of Computer Sciences Northeastern University Boston, MA 02115 EMAIL |
| Pseudocode | Yes | The pseudocode and detailed trajectory squeezing process for each proposed method are presented in Appendix C. |
| Open Source Code | Yes | In supplementary materials, we include the code and a README.txt file to reproduce the main experimental results. |
| Open Datasets | Yes | We investigate the performance of our algorithms over a variety of multi-agent problems with macroactions (Fig. 1): Box Pushing [Xiao et al., 2019], Overcooked [Wu et al., 2021b], and a larger Warehouse Tool Delivery [Xiao et al., 2019] domain. |
| Dataset Splits | No | The paper refers to 'training trials' and 'testing episodes' for evaluation but does not specify explicit dataset splits (e.g., percentages or counts for training, validation, and test sets). |
| Hardware Specification | Yes | The details of used computational resources are mentioned in Appendix E. |
| Software Dependencies | No | The provided text does not explicitly list software dependencies with specific version numbers. |
| Experiment Setup | Yes | All the training details including hyperparameters are in Appendix E. |